Software Engineer, Systems ML

Meta is seeking an AI Software Engineer to join our Research & Development teams. The ideal candidate will have industry experience working on AI Infrastructure related topics. The position will involve taking these skills and applying them to solve for some of the most crucial & exciting problems that exist on the web. We are hiring in multiple locations.

Apply relevant AI infrastructure and hardware acceleration techniques to build & optimize our intelligent ML systems that improve Meta’s products and experiences Goal setting related to project impact, AI system design, and infrastructure/developer efficiency Directly or influencing partners to deliver impact through deep, thorough data-driven analysis Drive large efforts across multiple teams Define use cases, and develop methodology & benchmarks to evaluate different approaches Apply in depth knowledge of how the ML infra interacts with the other systems around it Mentor other engineers / research scientists & improve the quality of engineering work in the broader team

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Specialized experience in one or more of the following machine learning/deep learning domains: Hardware accelerators architecture, GPU architecture, machine learning compilers, or ML systems, AI infrastructure, high performance computing, performance optimizations, or Machine learning frameworks (e.g. PyTorch), numerics and SW/HW co-design Experience developing AI-System infrastructure or AI algorithms in C/C++ or Python Master/PhD degree in Computer Science, Computer Engineering Technical leadership experience Experience with distributed systems or on-device algorithm development Experience with recommendation and ranking models Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies